• Open this web app: https://ibsneuro.shinyapps.io/palmer_penguins/

• Tab 1 contains information about the data set and lets you inspect the data frame

• Tab 2 allows you to generate plots by selecting the type of graph, which variables to put on the x and y axes and which variable to group by (using different colours)

• In your exploration, consider the questions on the following slide

• For each question, note down not only your answer but also the strategy you chose to get to it: how did you choose to construct your graph for the question and why?

• If you wanted to predict a penguin’s body mass, which other attributes could you look at (e.g. flipper length, bill length, sex etc.)? In other words, which of the other attributes appear to be most predictive of body mass?

• Is there a close relationship between bill length and bill depth?

• Is it possible to look at effects of island (i.e. the environment in which the penguins live) independently of other factors such as species or sex? If not, why not?

• For (the first part of) this workshop, we will be using Posit Cloud, which provides a web-based version of RStudio
• This means that you won’t have to install anything on your computer and that you will have direct access to all of the materials that I have prepared
• Go to https://posit.cloud and create a login if you haven’t already
• Once you have done this, use the link that you were emailed to access the uoc_data_science_2023 workspace on Posit Cloud and select the project data_science_2023
• When you access the project, you will receive your own copy of it to work on

For each of the following challenges, go back to the raw document (i.e. the one that doesn’t look pretty 😄), try to figure out how to make the relevant change and then render the document using Render to see whether you were correct!

• For the relationship between bill length and depth, change Gentoo to Adelie; check the figure to see if it worked
• Change the outcome variable in the histogram from body mass to something else and observe what happens. Remember that you can go back to the table at the top of the rendered document to have a look at the available variables. Note that the figure title will only change if you also adapt the text in “title”. (Hint: you may need to change the bin width! What would make sense for the outcome variable you have chosen?)
• Look at the effect of body mass by island rather than species. What do you see?
• Look at flipper length by sex rather than species
• Look at the effect of body mass by island rather than species. What do you see?
• Look at flipper length by sex rather than species

1. If you wanted to predict a penguin’s body mass, which other attributes could you look at (e.g. flipper length, bill length, sex etc.)? In other words, which of the other attributes appear to be most predictive of body mass? In addition to your answer, briefly note down how you went about answering this question.

2. Is there a close relationship between bill length and bill depth? Again, please describe briefly how you went about addressing this question. Also consider whether the answer might be a bit more complex than just “yes” or “no”.

3. Is it possible to look at effects of island (i.e. the environment in which the penguins live) independently of other factors such as species or sex? If not, why not?

4. Explore another 2 or 3 questions that interest you. Briefly describe these questions and what you found below.

5. Reflect on what the exercise (using the web app) has shown you regarding the use of different graph types to address different questions.

6. What were your first impressions when interacting with the RMarkdown document? For example … Were you able to make the changes requested in the exercise? What did you find easy? What did you find challenging?